Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures.
Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures.
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DOI：
10.1093/nar/gkq636
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发表时间：
2010-09
影响因子：
14.9
通讯作者：
Graeber TG
中科院分区：
文献类型：
--
作者：
Plaisier SB;Taschereau R;Wong JA;Graeber TG
比较独立的高通量基因表达实验可以产生关于哪些基因表达程序在特定生物过程之间共享的假设。目前比较表达谱的技术通常包括选择一个固定的差异表达阈值来总结结果，这可能会降低对微小但一致的变化的敏感性。我们提出了一种无阈值算法，称为秩秩超几何重叠（rho）。该算法根据两次谱分析实验中观察到的差异表达程度，逐级遍历两个基因列表，依次测量重叠基因数量的统计显著性。输出是一个图形化的地图，显示两个表达式概要之间的相关性的强度、模式和界限。为了证明RRHO的敏感性和动态范围，我们确定了癌症微阵列谱中驱动肿瘤进展、干细胞特性和对靶向激酶抑制反应的共享表达网络。我们演示了如何使用rho来确定哪种模型系统或药物治疗最能反映特定的生物或疾病反应。RRHO的无阈值和图形方面补充了其他基于等级的方法，如基因集富集分析（GSEA），其中RRHO是2D类似物。Rank-rank重叠分析是一种灵敏、稳健且可通过网络访问的方法，用于检测和可视化两个完整、连续的基因表达谱之间的重叠趋势。可以在http://systems.crump.ucla.edu/rankrank/访问基于web的rho实现。
Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We present a threshold-free algorithm called Rank–rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. To demonstrate RRHO sensitivity and dynamic range, we identified shared expression networks in cancer microarray profiles driving tumor progression, stem cell properties and response to targeted kinase inhibition. We demonstrate how RRHO can be used to determine which model system or drug treatment best reflects a particular biological or disease response. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank–rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles. A web-based implementation of RRHO can be accessed at http://systems.crump.ucla.edu/rankrank/.
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影响因子：
3.7
作者：
Hoshida Y;Brunet JP;Tamayo P;Golub TR;Mesirov JP
通讯作者：
Mesirov JP
影响因子：
11.4
作者：
Frank, O.;Brors, B.;Zheng, C.
通讯作者：
Zheng, C.
影响因子：
4.8
作者：
Fujita, N;Sato, S;Tsuruo, T
通讯作者：
Tsuruo, T
影响因子：
8
作者：
Landis, MD;Seachrist, DD;Keri, RA
通讯作者：
Keri, RA
影响因子：
50.3
作者：
Hieronymus, Haley;Lamb, Justin;Golub, Todd R.
通讯作者：
Golub, Todd R.